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Artificial and Human Intelligence through Learning: How Industry Applications Need Human-in-the-loop
University West, School of Business, Economics and IT, Divison of Informatics. (LINA iAIL)ORCID iD: 0000-0002-6101-3054
University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering. (LINA iAIL)ORCID iD: 0000-0001-7232-0079
University West, Department of Engineering Science, Division of Production Systems. (LINA iAIL PTW)ORCID iD: 0000-0001-8962-0924
University West, Department of Engineering Science, Division of Production Systems. (LINA iAIL PTW)ORCID iD: 0000-0003-0086-9067
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2020 (English)In: VILÄR: 3–4 December 2020 University West,Trollhättan. Abstracts / [ed] Kristina Johansson, Trollhättan: Högskolan Väst , 2020, p. 24-26Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

This study addresses work-integrated learning from a workplace learning perspective.Two companies within the manufacturing industry (turbo machinery and aerospace) together with a multi-disciplinary research group explore the opportunities and challenges related to applications of artificial intelligence and human intelligence and how such applications can integrate and support learning at the workplace.The manufacturing industry is currently under extreme pressure to transform their organizations and competencies to reap the benefits of industry 4.0. The main driverf or industry 4.0 is digitalization with disruptive technologies such as artificial intelligence, internet of things, machine learning, cyber-physical systems, digital platforms, etc. Many significant studies have highlighted the importance of human competence and learning in connection to industry 4.0 in general and disruptive technologies and its transformative consequences in particular. What impact have such technologies on employees and their workplace?

There is a lack of knowledge on how artificial intelligent systems actually take part in practices of human decision making and learning and to what extent disruptive technology may support both employees and organizations to “learn”. The design  and use of three real-world cases of artificial intelligence applications (as instances of industry 4.0 initiatives) will form the basis of how to support human decision making and scale up for strategic action and learning. Following a work-integratedapproach the overall research question has been formulated together with the two industry partners: How can artificial and human intelligence and learning, interact tobring manufacturing companies into Industry 4.0? An action-oriented research approach with in-depth qualitative and quantitative methods will be used in order to make sense and learn about new applications and data set related to a digitalized production.The contribution of this study will be three lessons learned along with a generic model for learning and organizing in the context of industry 4.0 initiatives. Tentative findings concern how artificial and human intelligence can be smartly integrated into the human work organization, i.e. the workplace. Many iterations of integrating the two intelligences are required. We will discuss a preliminary process-model called “Super8”, in which AI systems must allow for providing feedback on progress as well as being able to incorporate high-level human input in the learning process. The   practical implication of the study will be industrialized in the collaborating 

Place, publisher, year, edition, pages
Trollhättan: Högskolan Väst , 2020. p. 24-26
Keywords [en]
Artificial Intelligence, Human Intelligence, learning
National Category
Learning
Research subject
Work Integrated Learning
Identifiers
URN: urn:nbn:se:hv:diva-16142ISBN: 978-91-88847-86-7 (electronic)OAI: oai:DiVA.org:hv-16142DiVA, id: diva2:1511607
Conference
VILÄR. 3–4 December 2020 University West,Trollhättan
Note

The study was carried out within the AHIL-project, Artificial and Human Intelligence through Learning, funded by the Swedish Knowledge Foundation and University West

Available from: 2020-12-18 Created: 2020-12-18 Last updated: 2023-06-02Bibliographically approved

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Lundh Snis, Ulrikade Blanche, AndreasEriksson, Kristina M.Hattinger, MonikaOlsson, Anna KarinCarlsson, LinneaBelenki, Stanislav

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Lundh Snis, Ulrikade Blanche, AndreasEriksson, Kristina M.Hattinger, MonikaOlsson, Anna KarinCarlsson, LinneaBelenki, Stanislav
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